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chaos-engineering-altrupets-monorepo

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SKILL.md

💥 Skill: Chaos Engineering

📋 Metadata

Atributo Valor
ID sre-chaos-engineering
Nivel 🔴 Avanzado
Versión 1.0.0
Keywords chaos-engineering, chaos-monkey, litmus, failure-injection, resilience-testing, chaos-experiments
Referencia Chaos Engineering Principles

🔑 Keywords para Invocación

  • chaos-engineering
  • chaos-monkey
  • litmus
  • failure-injection
  • resilience-testing
  • chaos-experiments
  • @skill:chaos-engineering

Ejemplos de Prompts

Implementa chaos engineering con Litmus para resilience testing

Configura chaos experiments y failure injection

Setup Chaos Monkey para probar resiliencia del sistema

@skill:chaos-engineering - Chaos engineering completo

📖 Descripción

Chaos engineering es la práctica de inyectar fallos intencionalmente para probar la resiliencia de sistemas. Este skill cubre chaos experiments, failure injection, resilience testing, y herramientas como Litmus y Chaos Monkey.

✅ Cuándo Usar Este Skill

  • Sistemas en producción
  • Testing de resiliencia
  • Validación de failover
  • Identificación de puntos débiles
  • Mejora de reliability

❌ Cuándo NO Usar Este Skill

  • Sistemas en desarrollo temprano
  • Sin monitoring adecuado
  • Sin rollback procedures
  • Sin equipo preparado

🏗️ Chaos Engineering Process

Hypothesis
    ↓
Experiment Design
    ↓
Execute Experiment
    ↓
Observe & Measure
    ↓
Learn & Improve

💻 Implementación

📁 Scripts Ejecutables: Este skill incluye scripts ejecutables en la carpeta scripts/:

  • Chaos Monkey: scripts/chaos_monkey.py - Chaos Monkey para Kubernetes
  • Experiments: scripts/experiments.py - Framework para chaos experiments

Ver scripts/README.md para documentación de uso completa.

1. Litmus Chaos Experiments

yaml
# chaos/pod-delete-experiment.yaml
apiVersion: litmuschaos.io/v1alpha1
kind: ChaosEngine
metadata:
  name: pod-delete-chaos
  namespace: default
spec:
  annotationCheck: 'true'
  engineState: 'active'
  chaosServiceAccount: litmus-admin
  monitoring: true
  jobCleanUpPolicy: 'retain'
  experiments:
    - name: pod-delete
      spec:
        components:
          env:
            - name: TOTAL_CHAOS_DURATION
              value: '60'
            - name: CHAOS_INTERVAL
              value: '10'
            - name: FORCE
              value: 'false'
            - name: RAMP_TIME
              value: '10'
        probe:
          - name: check-app-health
            type: httpProbe
            httpProbeInputs:
              url: http://app-service/health
              insecureSkipVerify: false
            mode: Continuous
            runProperties:
              probeTimeout: 5
              interval: 2
              retry: 1
yaml
# chaos/network-chaos.yaml
apiVersion: litmuschaos.io/v1alpha1
kind: ChaosEngine
metadata:
  name: network-chaos
spec:
  engineState: 'active'
  chaosServiceAccount: litmus-admin
  experiments:
    - name: network-chaos
      spec:
        components:
          env:
            - name: NETWORK_INTERFACE
              value: 'eth0'
            - name: NETWORK_PACKET_LOSS_PERCENTAGE
              value: '100'
            - name: TARGET_CONTAINER
              value: 'app-container'
            - name: TARGET_PODS
              value: 'app-.*'
            - name: TOTAL_CHAOS_DURATION
              value: '120'

2. Chaos Monkey Implementation

Script ejecutable: scripts/chaos_monkey.py

Chaos Monkey para Kubernetes que elimina aleatoriamente pods y recursos para probar resiliencia.

Cuándo ejecutar:

  • Testing de resiliencia en producción
  • Validación de failover
  • Identificación de puntos débiles

Uso:

bash
# Ejecutar experimento (pod-delete)
python scripts/chaos_monkey.py run --experiment-type pod-delete

# Habilitar/deshabilitar
python scripts/chaos_monkey.py enable
python scripts/chaos_monkey.py disable

# Configurar probabilidad
python scripts/chaos_monkey.py set-probability --probability 0.1

Características:

  • ✅ Eliminación aleatoria de pods
  • ✅ Probabilidad configurable
  • ✅ Filtrado por annotations (chaos.enabled=true)
  • ✅ Múltiples tipos de experimentos

3. Chaos Experiments

Script ejecutable: scripts/experiments.py

Framework para ejecutar y gestionar chaos experiments con hipótesis y resultados.

Cuándo ejecutar:

  • Ejecución de experiments estructurados
  • Gestión de múltiples experiments
  • Registro de nuevos experiments

Uso:

bash
# Ejecutar experimento
python scripts/experiments.py run --name pod-delete

# Listar experimentos
python scripts/experiments.py list

Características:

  • ✅ Framework de experiments estructurado
  • ✅ Hipótesis y expected behavior
  • ✅ Tracking de resultados
  • ✅ Registro de experiments

4. Automated Chaos Testing

yaml
# chaos/chaos-test-schedule.yaml
apiVersion: batch/v1
kind: CronJob
metadata:
  name: chaos-monkey
spec:
  schedule: "0 */6 * * *"  # Every 6 hours
  jobTemplate:
    spec:
      template:
        spec:
          containers:
          - name: chaos-monkey
            image: chaos-monkey:latest
            env:
            - name: ENABLED
              value: "true"
            - name: PROBABILITY
              value: "0.1"
            - name: EXPERIMENT_TYPES
              value: "pod-delete,cpu-stress"
            command:
            - python
            - chaos_monkey.py
            - --schedule
          restartPolicy: OnFailure

5. Chaos Metrics

python
# chaos/metrics.py
from prometheus_client import Counter, Histogram, Gauge

# Metrics
chaos_experiments_total = Counter(
    'chaos_experiments_total',
    'Total chaos experiments run',
    ['experiment_type', 'status']
)

chaos_experiment_duration = Histogram(
    'chaos_experiment_duration_seconds',
    'Duration of chaos experiments',
    ['experiment_type']
)

system_recovery_time = Histogram(
    'system_recovery_time_seconds',
    'Time for system to recover from chaos',
    ['experiment_type']
)

chaos_experiments_active = Gauge(
    'chaos_experiments_active',
    'Number of active chaos experiments'
)

def record_experiment(experiment_type: str, status: str, duration: float):
    """Record chaos experiment metrics."""
    chaos_experiments_total.labels(
        experiment_type=experiment_type,
        status=status
    ).inc()

    chaos_experiment_duration.labels(
        experiment_type=experiment_type
    ).observe(duration)

🎯 Mejores Prácticas

1. Experiment Design

✅ DO:

  • Start with hypothesis
  • Test in non-production first
  • Start small and increase
  • Monitor during experiments

❌ DON'T:

  • Run experiments without hypothesis
  • Start in production
  • Run multiple experiments simultaneously
  • Ignore monitoring

2. Safety

✅ DO:

  • Use feature flags
  • Have rollback procedures
  • Set experiment duration limits
  • Monitor system health

❌ DON'T:

  • Run experiments without safety measures
  • Ignore system health
  • Exceed experiment duration
  • Skip rollback procedures

3. Learning

✅ DO:

  • Document results
  • Share learnings
  • Improve based on results
  • Regular experiments

❌ DON'T:

  • Skip documentation
  • Ignore results
  • Run experiments randomly
  • Stop after first success

🚨 Troubleshooting

Experiments Causing Issues

  1. Stop experiment immediately
  2. Review experiment design
  3. Check system health
  4. Adjust experiment parameters

No Recovery

  1. Check failover mechanisms
  2. Review monitoring
  3. Investigate root cause
  4. Fix underlying issues

📚 Recursos Adicionales

Versión: 1.0.0 Última actualización: Diciembre 2025 Total líneas: 1,100+

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